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Comprehensive guide for developing WebGPU-enabled Three.js applications using TSL (Three.js Shading Language). Covers WebGPU renderer setup, TSL syntax and node materials, compute shaders, post-processing effects, and WGSL integration. Use this skill when working with Three.js WebGPU, TSL shaders, node materials, or GPU compute in Three.js.
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Reference WebGPU APIs, TSL syntax, node materials, WGSL integration, compute shaders, and post-processing in Three.js. For complete particle canvases use threejs-particle-canvas; for GLSL shaders use rocaille-shader. Triggers on TSL shader, WebGPU renderer, node material, WGSL, GPU compute, WebGPU setup.
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Pulp in the browser — the WAM v2 and WebCLAP adapters, the wasm runtime, the Skia/WebGL2 browser window host, and the WebGPU (emdawnwebgpu) GPU-audio lane. Covers what does and does not compile to wasm, the worklet-thread constraints (no std::thread, no fetch, no navigator.gpu), the non-realtime tick, memory growth, how to PROVE a GPU lane actually ran rather than silently falling back to CPU, and the silent-failure traps that make a wasm build "work" while producing no audio or no pixels.
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Execute model inference on GPU cloud providers. Handles code generation, deployment, execution, and result collection across HF Inference API/Endpoints, Colab, Modal, beam.cloud, Vast.ai, and RunPod. Use when running models on GPU, deploying to cloud, executing notebooks, or troubleshooting GPU execution failures. Triggers on "run on GPU", "execute model", "deploy to modal", "colab notebook", "beam deploy", "HF inference", "HF endpoints", "vast", "runpod".
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Plan or audit the cross-layer Harbor cooperative editor: Loro replicas for humans and agents, governed edit claims, salvage authority, and transport across shared, LAN, and remote harbors. Use when a change spans editor collaboration, identity, recovery, or transport contracts. For a single GPUI pane use gpui-rust-console; for motion use rust-gpui-motion; for a shader use gpui-shaders. NOT for non-collaborative screens, web editors, or generic Rust development.
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Comprehensive guide for developing WebGPU-enabled Three.js applications using TSL (Three.js Shading Language). Covers WebGPU renderer setup, node materials, compute shaders, post-processing, and WGSL integration. Use when working with Three.js WebGPU, TSL shaders, or GPU compute pipelines.
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使用 NeMo Curator 整理大语言模型训练数据，去除重复和低质量内容，删除或替换个人信息。还涵盖处理流程的构建，以及图像、视频和音频数据的筛选整理。
Build HyperFrames animations from small motion recipes, scene templates, and transitions. The skill guides runtime selection, including GSAP, Lottie, and Three.js, and provides an audit script to examine timing and choreography while following HyperFrames requirements for reproducible rendering.
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Use hosted DNA models to predict gene structures, expression levels, and regions that regulate gene activity. Start from a gene symbol, genomic coordinates, or a DNA sequence, without installing model weights or running a local GPU.
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Follow guidance to create and operate Kubernetes clusters on Azure Kubernetes Service. Configure groups of worker machines, automatic scaling, networking, monitoring, identity integration, and upgrades, with command examples and Terraform configuration for repeatable setup.
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Design traffic controls for AI inference services using Istio configuration examples. Covers mutual authentication, model version routing, gradual releases, retry limits, failure containment, and monitoring, with guidance for validating changes on the target cluster.
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Add a diffusion model or Diffusers generation pipeline to SGLang. Examine reference code, choose a suitable pipeline structure, reuse existing components, and implement model settings and processing stages, with guidance for later multi-GPU support.
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Convert LLMs to GGUF and reduce model size through quantization, which stores weights at lower numerical precision. Prepare models for CPU or Apple Silicon inference, compare compression levels, and integrate them with local AI tools.
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Guide reinforcement learning, training models through rewards, with slime. Prepare data, configure Megatron-LM training and SGLang response generation, and set up asynchronous runs or custom multi-turn workflows for supported language models.
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Build image captioning, visual question answering, and text-based image search with BLIP-2. The skill explains model selection, batch processing, and memory-saving settings, with Python examples for connecting image understanding to language models.
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Get guidance on distributed training with PyTorch FSDP, which splits model parameters across devices. The skill covers implementation, mixed precision, moving data to CPU memory, communication setup, and debugging distributed training code.
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Coordinate a research project from a broad topic through literature review, idea selection, experiments, and iterative review. Produce a research summary for writing, with optional paper drafting and PDF compilation, while recording stage progress for later resumption.
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Monitor model training through WandB metrics to detect invalid numbers, worsening loss, and declining evaluation results. Ambiguous trends receive a Codex review, while stopped runs retain key metrics and the reason for stopping.
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Guides LLM compression through pruning, which removes selected model weights. Covers Wanda and SparseGPT methods without retraining, accuracy comparisons, and structured weight patterns that can accelerate inference on compatible hardware.
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Implement Mamba, a model architecture that processes sequences through an internal state with computation scaling linearly with input length. Covers pretrained text generation, Mamba-1 and Mamba-2 configuration, speed comparisons with Transformers, and GPU memory troubleshooting.
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Choose a serving container for models deployed on SageMaker. Match the model type to a compatible image, retrieve its address from the AWS catalog, and check regional availability, CPU or GPU variants, and required host settings.
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Check whether a Dynamo deployment is ready for disaggregated serving, where input processing and token generation run separately. Read-only probes inspect transport settings, GPU networking capabilities, and NIXL transfer-test readiness, then report gaps and next steps.
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Add animated backgrounds such as waves or fog with Vanta.js, which renders graphics through WebGL. Configure effects, integrate them into pages or React components, handle resizing and cleanup, and plan fallbacks for mobile devices.
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Add cursor-following water-ripple distortion to existing images using Shaders components. Preserve page content and layout while integrating lazy loading, accessible image fallbacks, and static display when WebGPU is unavailable or reduced motion is enabled.
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